Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations505354
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory119.5 MiB
Average record size in memory248.0 B

Variable types

Numeric14
Categorical17

Alerts

Avg_Hillshade is highly overall correlated with Hillshade_3pm and 2 other fieldsHigh correlation
Cover_Type is highly overall correlated with Soil_Type_9 and 1 other fieldsHigh correlation
Distance_to_Water is highly overall correlated with Horizontal_Distance_To_Hydrology and 1 other fieldsHigh correlation
Elevation is highly overall correlated with Soil_Type_39 and 2 other fieldsHigh correlation
Elevation_x_Slope is highly overall correlated with SlopeHigh correlation
Hillshade_3pm is highly overall correlated with Avg_Hillshade and 2 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Avg_Hillshade and 1 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points is highly overall correlated with Hydro_Road_Fire_DistanceHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Distance_to_Water and 1 other fieldsHigh correlation
Horizontal_Distance_To_Roadways is highly overall correlated with Hydro_Road_Fire_DistanceHigh correlation
Hydro_Road_Fire_Distance is highly overall correlated with Horizontal_Distance_To_Fire_Points and 3 other fieldsHigh correlation
Slope is highly overall correlated with Avg_Hillshade and 1 other fieldsHigh correlation
Soil_Type_28 is highly overall correlated with Wilderness_Area_0High correlation
Soil_Type_39 is highly overall correlated with ElevationHigh correlation
Soil_Type_9 is highly overall correlated with Cover_Type and 2 other fieldsHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Distance_to_Water and 1 other fieldsHigh correlation
Wilderness_Area_0 is highly overall correlated with Hydro_Road_Fire_Distance and 2 other fieldsHigh correlation
Wilderness_Area_2 is highly overall correlated with Wilderness_Area_0High correlation
Wilderness_Area_3 is highly overall correlated with Cover_Type and 3 other fieldsHigh correlation
Wilderness_Area_3 is highly imbalanced (62.2%) Imbalance
Soil_Type_3 is highly imbalanced (88.7%) Imbalance
Soil_Type_5 is highly imbalanced (90.0%) Imbalance
Soil_Type_9 is highly imbalanced (70.0%) Imbalance
Soil_Type_10 is highly imbalanced (88.0%) Imbalance
Soil_Type_11 is highly imbalanced (67.5%) Imbalance
Soil_Type_12 is highly imbalanced (82.4%) Imbalance
Soil_Type_21 is highly imbalanced (71.0%) Imbalance
Soil_Type_22 is highly imbalanced (54.3%) Imbalance
Soil_Type_29 is highly imbalanced (67.4%) Imbalance
Soil_Type_31 is highly imbalanced (55.7%) Imbalance
Soil_Type_37 is highly imbalanced (82.2%) Imbalance
Soil_Type_38 is highly imbalanced (85.5%) Imbalance
Soil_Type_39 is highly imbalanced (89.7%) Imbalance
Horizontal_Distance_To_Hydrology has 21630 (4.3%) zeros Zeros
Vertical_Distance_To_Hydrology has 34287 (6.8%) zeros Zeros
Distance_to_Water has 21630 (4.3%) zeros Zeros

Reproduction

Analysis started2025-06-09 15:39:43.484309
Analysis finished2025-06-09 15:40:31.678569
Duration48.19 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

High correlation 

Distinct1978
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2951.5881
Minimum1859
Maximum3858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:31.744649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1859
5-th percentile2381
Q12814
median2987
Q33142
95-th percentile3322
Maximum3858
Range1999
Interquartile range (IQR)328

Descriptive statistics

Standard deviation275.48424
Coefficient of variation (CV)0.093334245
Kurtosis1.0931128
Mean2951.5881
Median Absolute Deviation (MAD)163
Skewness-0.89179409
Sum1.4915968 × 109
Variance75891.569
MonotonicityNot monotonic
2025-06-09T18:40:31.832671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2968 1625
 
0.3%
2962 1613
 
0.3%
2991 1613
 
0.3%
2972 1609
 
0.3%
2978 1603
 
0.3%
2975 1599
 
0.3%
2988 1554
 
0.3%
2955 1524
 
0.3%
2965 1519
 
0.3%
2952 1514
 
0.3%
Other values (1968) 489581
96.9%
ValueCountFrequency (%)
1859 1
 
< 0.1%
1860 1
 
< 0.1%
1861 1
 
< 0.1%
1863 1
 
< 0.1%
1866 1
 
< 0.1%
1867 1
 
< 0.1%
1868 1
 
< 0.1%
1871 3
< 0.1%
1872 4
< 0.1%
1873 1
 
< 0.1%
ValueCountFrequency (%)
3858 2
 
< 0.1%
3857 1
 
< 0.1%
3856 1
 
< 0.1%
3853 1
 
< 0.1%
3852 1
 
< 0.1%
3851 2
 
< 0.1%
3850 1
 
< 0.1%
3849 4
< 0.1%
3848 1
 
< 0.1%
3846 6
< 0.1%

Slope
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.767395
Minimum0
Maximum66
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:31.929186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median13
Q318
95-th percentile28
Maximum66
Range66
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.4737982
Coefficient of variation (CV)0.5428622
Kurtosis0.75038989
Mean13.767395
Median Absolute Deviation (MAD)5
Skewness0.85685891
Sum6957408
Variance55.857659
MonotonicityNot monotonic
2025-06-09T18:40:32.015451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 30511
 
6.0%
11 30123
 
6.0%
12 29324
 
5.8%
9 29017
 
5.7%
13 28187
 
5.6%
8 27610
 
5.5%
14 25932
 
5.1%
15 24691
 
4.9%
7 24304
 
4.8%
6 22768
 
4.5%
Other values (57) 232887
46.1%
ValueCountFrequency (%)
0 621
 
0.1%
1 3485
 
0.7%
2 7301
 
1.4%
3 10951
 
2.2%
4 15310
3.0%
5 19417
3.8%
6 22768
4.5%
7 24304
4.8%
8 27610
5.5%
9 29017
5.7%
ValueCountFrequency (%)
66 1
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
63 1
 
< 0.1%
62 2
 
< 0.1%
61 4
< 0.1%
60 2
 
< 0.1%
59 3
< 0.1%
58 1
 
< 0.1%
57 7
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.15709
Minimum0
Maximum1397
Zeros21630
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:32.102793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median216
Q3379
95-th percentile674
Maximum1397
Range1397
Interquartile range (IQR)271

Descriptive statistics

Standard deviation211.08873
Coefficient of variation (CV)0.79309826
Kurtosis1.6070835
Mean266.15709
Median Absolute Deviation (MAD)131
Skewness1.188776
Sum1.3450355 × 108
Variance44558.451
MonotonicityNot monotonic
2025-06-09T18:40:32.188798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 29993
 
5.9%
0 21630
 
4.3%
150 18274
 
3.6%
60 16778
 
3.3%
67 13454
 
2.7%
42 12960
 
2.6%
108 12702
 
2.5%
85 12156
 
2.4%
90 9757
 
1.9%
120 9343
 
1.8%
Other values (541) 348307
68.9%
ValueCountFrequency (%)
0 21630
4.3%
30 29993
5.9%
42 12960
2.6%
60 16778
3.3%
67 13454
2.7%
85 12156
2.4%
90 9757
 
1.9%
95 8120
 
1.6%
108 12702
2.5%
120 9343
 
1.8%
ValueCountFrequency (%)
1397 1
< 0.1%
1390 2
< 0.1%
1383 2
< 0.1%
1382 1
< 0.1%
1376 1
< 0.1%
1371 1
< 0.1%
1370 1
< 0.1%
1369 1
< 0.1%
1368 2
< 0.1%
1361 2
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct692
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.002424
Minimum-159
Maximum601
Zeros34287
Zeros (%)6.8%
Negative45771
Negative (%)9.1%
Memory size3.9 MiB
2025-06-09T18:40:32.287112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-159
5-th percentile-7
Q17
median29
Q367
95-th percentile164
Maximum601
Range760
Interquartile range (IQR)60

Descriptive statistics

Standard deviation57.90111
Coefficient of variation (CV)1.2586535
Kurtosis5.8017203
Mean46.002424
Median Absolute Deviation (MAD)26
Skewness1.8979445
Sum23247509
Variance3352.5386
MonotonicityNot monotonic
2025-06-09T18:40:32.374627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34287
 
6.8%
3 8388
 
1.7%
10 8084
 
1.6%
7 7807
 
1.5%
13 7707
 
1.5%
6 7694
 
1.5%
4 7529
 
1.5%
5 6717
 
1.3%
16 6715
 
1.3%
23 6539
 
1.3%
Other values (682) 403887
79.9%
ValueCountFrequency (%)
-159 2
< 0.1%
-158 1
< 0.1%
-156 1
< 0.1%
-154 1
< 0.1%
-153 2
< 0.1%
-152 2
< 0.1%
-151 1
< 0.1%
-150 1
< 0.1%
-149 1
< 0.1%
-147 1
< 0.1%
ValueCountFrequency (%)
601 1
 
< 0.1%
599 1
 
< 0.1%
598 2
< 0.1%
597 3
< 0.1%
595 2
< 0.1%
592 1
 
< 0.1%
591 1
 
< 0.1%
590 2
< 0.1%
589 3
< 0.1%
588 3
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ)

High correlation 

Distinct5785
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2431.98
Minimum0
Maximum7117
Zeros96
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:32.463653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile391
Q11140
median2078
Q33475
95-th percentile5571
Maximum7117
Range7117
Interquartile range (IQR)2335

Descriptive statistics

Standard deviation1600.4154
Coefficient of variation (CV)0.65807093
Kurtosis-0.53780115
Mean2431.98
Median Absolute Deviation (MAD)1085
Skewness0.65607631
Sum1.2290108 × 109
Variance2561329.4
MonotonicityNot monotonic
2025-06-09T18:40:32.553586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1078
 
0.2%
618 882
 
0.2%
900 821
 
0.2%
1020 805
 
0.2%
990 777
 
0.2%
960 764
 
0.2%
390 763
 
0.2%
1140 736
 
0.1%
1050 726
 
0.1%
750 725
 
0.1%
Other values (5775) 497277
98.4%
ValueCountFrequency (%)
0 96
 
< 0.1%
30 267
0.1%
42 153
 
< 0.1%
60 280
0.1%
67 249
< 0.1%
85 293
0.1%
90 331
0.1%
95 317
0.1%
108 537
0.1%
120 559
0.1%
ValueCountFrequency (%)
7117 1
< 0.1%
7116 1
< 0.1%
7112 1
< 0.1%
7097 1
< 0.1%
7092 1
< 0.1%
7087 2
< 0.1%
7082 1
< 0.1%
7079 1
< 0.1%
7078 2
< 0.1%
7069 1
< 0.1%

Hillshade_9am
Real number (ℝ)

High correlation 

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.28271
Minimum0
Maximum254
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:32.641726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160
Q1199
median218
Q3231
95-th percentile245
Maximum254
Range254
Interquartile range (IQR)32

Descriptive statistics

Standard deviation26.629387
Coefficient of variation (CV)0.12544303
Kurtosis2.1182007
Mean212.28271
Median Absolute Deviation (MAD)15
Skewness-1.2439666
Sum1.0727792 × 108
Variance709.12424
MonotonicityNot monotonic
2025-06-09T18:40:32.732238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 10413
 
2.1%
228 10242
 
2.0%
224 10051
 
2.0%
230 10049
 
2.0%
223 9782
 
1.9%
222 9720
 
1.9%
233 9416
 
1.9%
227 9382
 
1.9%
225 9243
 
1.8%
221 9239
 
1.8%
Other values (197) 407817
80.7%
ValueCountFrequency (%)
0 13
< 0.1%
36 1
 
< 0.1%
46 2
 
< 0.1%
50 1
 
< 0.1%
52 1
 
< 0.1%
53 1
 
< 0.1%
54 3
 
< 0.1%
55 1
 
< 0.1%
56 5
 
< 0.1%
57 2
 
< 0.1%
ValueCountFrequency (%)
254 1641
 
0.3%
253 1822
 
0.4%
252 2123
0.4%
251 2409
0.5%
250 2766
0.5%
249 3116
0.6%
248 3249
0.6%
247 3691
0.7%
246 4069
0.8%
245 4560
0.9%

Hillshade_Noon
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.2563
Minimum0
Maximum254
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:32.840524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile187
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.58077
Coefficient of variation (CV)0.087705341
Kurtosis2.4443385
Mean223.2563
Median Absolute Deviation (MAD)12
Skewness-1.1404263
Sum1.1282347 × 108
Variance383.40657
MonotonicityNot monotonic
2025-06-09T18:40:32.938043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231 12314
 
2.4%
228 12230
 
2.4%
233 12063
 
2.4%
230 11975
 
2.4%
229 11849
 
2.3%
234 11805
 
2.3%
227 11601
 
2.3%
226 11594
 
2.3%
223 11564
 
2.3%
225 11495
 
2.3%
Other values (175) 386864
76.6%
ValueCountFrequency (%)
0 5
< 0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
45 1
 
< 0.1%
53 2
 
< 0.1%
63 1
 
< 0.1%
64 1
 
< 0.1%
68 1
 
< 0.1%
71 1
 
< 0.1%
ValueCountFrequency (%)
254 3981
0.8%
253 4664
0.9%
252 5492
1.1%
251 5864
1.2%
250 6488
1.3%
249 6356
1.3%
248 6907
1.4%
247 7617
1.5%
246 7453
1.5%
245 7352
1.5%

Hillshade_3pm
Real number (ℝ)

High correlation 

Distinct255
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.57913
Minimum0
Maximum254
Zeros1266
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:33.033560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q1120
median143
Q3168
95-th percentile203
Maximum254
Range254
Interquartile range (IQR)48

Descriptive statistics

Standard deviation37.78155
Coefficient of variation (CV)0.26498653
Kurtosis0.54411121
Mean142.57913
Median Absolute Deviation (MAD)24
Skewness-0.29140078
Sum72052935
Variance1427.4455
MonotonicityNot monotonic
2025-06-09T18:40:33.125085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 6582
 
1.3%
145 6474
 
1.3%
138 6384
 
1.3%
146 6243
 
1.2%
142 6189
 
1.2%
136 6166
 
1.2%
139 6153
 
1.2%
149 6078
 
1.2%
135 6035
 
1.2%
150 6026
 
1.2%
Other values (245) 443024
87.7%
ValueCountFrequency (%)
0 1266
0.3%
1 14
 
< 0.1%
2 15
 
< 0.1%
3 15
 
< 0.1%
4 18
 
< 0.1%
5 18
 
< 0.1%
6 24
 
< 0.1%
7 28
 
< 0.1%
8 20
 
< 0.1%
9 31
 
< 0.1%
ValueCountFrequency (%)
254 4
 
< 0.1%
253 8
 
< 0.1%
252 16
 
< 0.1%
251 10
 
< 0.1%
250 15
 
< 0.1%
249 35
< 0.1%
248 41
< 0.1%
247 54
< 0.1%
246 68
< 0.1%
245 78
< 0.1%

Horizontal_Distance_To_Fire_Points
Real number (ℝ)

High correlation 

Distinct5827
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.1361
Minimum0
Maximum7173
Zeros45
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:33.210600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile408
Q11024
median1725
Q32592
95-th percentile5089
Maximum7173
Range7173
Interquartile range (IQR)1568

Descriptive statistics

Standard deviation1357.8179
Coefficient of variation (CV)0.67548557
Kurtosis1.5080032
Mean2010.1361
Median Absolute Deviation (MAD)771
Skewness1.266097
Sum1.0158303 × 109
Variance1843669.5
MonotonicityNot monotonic
2025-06-09T18:40:33.306947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618 1244
 
0.2%
541 981
 
0.2%
607 930
 
0.2%
942 856
 
0.2%
997 844
 
0.2%
700 833
 
0.2%
726 798
 
0.2%
752 782
 
0.2%
900 774
 
0.2%
960 768
 
0.2%
Other values (5817) 496544
98.3%
ValueCountFrequency (%)
0 45
 
< 0.1%
30 184
< 0.1%
42 183
< 0.1%
60 182
< 0.1%
67 370
0.1%
85 183
< 0.1%
90 182
< 0.1%
95 366
0.1%
108 369
0.1%
120 180
< 0.1%
ValueCountFrequency (%)
7173 1
< 0.1%
7172 1
< 0.1%
7168 1
< 0.1%
7150 1
< 0.1%
7145 1
< 0.1%
7142 1
< 0.1%
7141 2
< 0.1%
7140 1
< 0.1%
7131 1
< 0.1%
7126 1
< 0.1%

Wilderness_Area_0
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
1.0
260796 
0.0
244558 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 260796
51.6%
0.0 244558
48.4%

Length

2025-06-09T18:40:33.382951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:33.434456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 260796
51.6%
0.0 244558
48.4%

Most occurring characters

ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 749912
49.5%
. 505354
33.3%
1 260796
 
17.2%

Wilderness_Area_2
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
306193 
1.0
199161 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 306193
60.6%
1.0 199161
39.4%

Length

2025-06-09T18:40:33.484461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:33.522850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 306193
60.6%
1.0 199161
39.4%

Most occurring characters

ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 811547
53.5%
. 505354
33.3%
1 199161
 
13.1%

Wilderness_Area_3
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
468386 
1.0
 
36968

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 468386
92.7%
1.0 36968
 
7.3%

Length

2025-06-09T18:40:33.571645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:33.609158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 468386
92.7%
1.0 36968
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 973740
64.2%
. 505354
33.3%
1 36968
 
2.4%

Soil_Type_3
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
497735 
1.0
 
7619

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 497735
98.5%
1.0 7619
 
1.5%

Length

2025-06-09T18:40:33.655156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:33.693163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 497735
98.5%
1.0 7619
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1003089
66.2%
. 505354
33.3%
1 7619
 
0.5%

Soil_Type_5
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
498779 
1.0
 
6575

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 498779
98.7%
1.0 6575
 
1.3%

Length

2025-06-09T18:40:33.739672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:33.777672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 498779
98.7%
1.0 6575
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1004133
66.2%
. 505354
33.3%
1 6575
 
0.4%

Soil_Type_9
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
478425 
1.0
 
26929

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 478425
94.7%
1.0 26929
 
5.3%

Length

2025-06-09T18:40:33.825996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:33.863997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 478425
94.7%
1.0 26929
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 983779
64.9%
. 505354
33.3%
1 26929
 
1.8%

Soil_Type_10
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
497100 
1.0
 
8254

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 497100
98.4%
1.0 8254
 
1.6%

Length

2025-06-09T18:40:33.910506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:33.949508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 497100
98.4%
1.0 8254
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1002454
66.1%
. 505354
33.3%
1 8254
 
0.5%

Soil_Type_11
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
475383 
1.0
 
29971

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 475383
94.1%
1.0 29971
 
5.9%

Length

2025-06-09T18:40:33.998747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:34.045058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 475383
94.1%
1.0 29971
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 980737
64.7%
. 505354
33.3%
1 29971
 
2.0%

Soil_Type_12
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
492001 
1.0
 
13353

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 492001
97.4%
1.0 13353
 
2.6%

Length

2025-06-09T18:40:34.097566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:34.137307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 492001
97.4%
1.0 13353
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 997355
65.8%
. 505354
33.3%
1 13353
 
0.9%

Soil_Type_21
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
479672 
1.0
 
25682

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 479672
94.9%
1.0 25682
 
5.1%

Length

2025-06-09T18:40:34.186309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:34.225815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 479672
94.9%
1.0 25682
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 985026
65.0%
. 505354
33.3%
1 25682
 
1.7%

Soil_Type_22
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
456668 
1.0
48686 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 456668
90.4%
1.0 48686
 
9.6%

Length

2025-06-09T18:40:34.277815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:34.318108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 456668
90.4%
1.0 48686
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 962022
63.5%
. 505354
33.3%
1 48686
 
3.2%

Soil_Type_28
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
390180 
1.0
115174 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 390180
77.2%
1.0 115174
 
22.8%

Length

2025-06-09T18:40:34.368107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:34.408622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 390180
77.2%
1.0 115174
 
22.8%

Most occurring characters

ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 895534
59.1%
. 505354
33.3%
1 115174
 
7.6%

Soil_Type_29
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
475184 
1.0
 
30170

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 475184
94.0%
1.0 30170
 
6.0%

Length

2025-06-09T18:40:34.458618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:34.496622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 475184
94.0%
1.0 30170
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 980538
64.7%
. 505354
33.3%
1 30170
 
2.0%

Soil_Type_31
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
458914 
1.0
46440 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 458914
90.8%
1.0 46440
 
9.2%

Length

2025-06-09T18:40:34.543131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:34.583922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 458914
90.8%
1.0 46440
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 964268
63.6%
. 505354
33.3%
1 46440
 
3.1%

Soil_Type_37
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
491835 
1.0
 
13519

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 491835
97.3%
1.0 13519
 
2.7%

Length

2025-06-09T18:40:34.633430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:34.671431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 491835
97.3%
1.0 13519
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 997189
65.8%
. 505354
33.3%
1 13519
 
0.9%

Soil_Type_38
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
494948 
1.0
 
10406

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 494948
97.9%
1.0 10406
 
2.1%

Length

2025-06-09T18:40:34.719705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:34.758701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 494948
97.9%
1.0 10406
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000302
66.0%
. 505354
33.3%
1 10406
 
0.7%

Soil_Type_39
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
498520 
1.0
 
6834

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 498520
98.6%
1.0 6834
 
1.4%

Length

2025-06-09T18:40:34.806982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T18:40:34.845983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 498520
98.6%
1.0 6834
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1003874
66.2%
. 505354
33.3%
1 6834
 
0.5%

Distance_to_Water
Real number (ℝ)

High correlation  Zeros 

Distinct48761
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.713
Minimum0
Maximum1418.9168
Zeros21630
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:34.900497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108.16654
median225.85836
Q3390.10383
95-th percentile689.3271
Maximum1418.9168
Range1418.9168
Interquartile range (IQR)281.93729

Descriptive statistics

Standard deviation215.62566
Coefficient of variation (CV)0.7906688
Kurtosis1.604842
Mean272.713
Median Absolute Deviation (MAD)135.58655
Skewness1.1832935
Sum1.3781661 × 108
Variance46494.427
MonotonicityNot monotonic
2025-06-09T18:40:34.982497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21630
 
4.3%
30 4151
 
0.8%
30.14962686 3374
 
0.7%
30.01666204 3338
 
0.7%
30.06659276 3333
 
0.7%
30.2654919 2748
 
0.5%
30.41381265 2308
 
0.5%
30.59411708 2177
 
0.4%
30.8058436 1810
 
0.4%
31.04834939 1299
 
0.3%
Other values (48751) 459186
90.9%
ValueCountFrequency (%)
0 21630
4.3%
30 4151
 
0.8%
30.01666204 3338
 
0.7%
30.06659276 3333
 
0.7%
30.14962686 3374
 
0.7%
30.2654919 2748
 
0.5%
30.41381265 2308
 
0.5%
30.59411708 2177
 
0.4%
30.8058436 1810
 
0.4%
31.04834939 1299
 
0.3%
ValueCountFrequency (%)
1418.91684 1
< 0.1%
1413.295794 1
< 0.1%
1411.059531 1
< 0.1%
1407.459058 1
< 0.1%
1397.781456 1
< 0.1%
1395.054838 1
< 0.1%
1394.460469 1
< 0.1%
1390.893598 1
< 0.1%
1389.705724 1
< 0.1%
1384.234084 1
< 0.1%

Avg_Hillshade
Real number (ℝ)

High correlation 

Distinct384
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.70605
Minimum31.666667
Maximum213.66667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:35.067727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31.666667
5-th percentile165.33333
Q1185.66667
median195.33333
Q3202.66667
95-th percentile211
Maximum213.66667
Range182
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.393874
Coefficient of variation (CV)0.074693421
Kurtosis3.0701699
Mean192.70605
Median Absolute Deviation (MAD)8.3333333
Skewness-1.3616708
Sum97384772
Variance207.18361
MonotonicityNot monotonic
2025-06-09T18:40:35.156015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199.3333333 6583
 
1.3%
198 6186
 
1.2%
198.6666667 6168
 
1.2%
201.3333333 5970
 
1.2%
196.3333333 5963
 
1.2%
200 5952
 
1.2%
195.6666667 5878
 
1.2%
195.3333333 5798
 
1.1%
196.6666667 5781
 
1.1%
200.6666667 5696
 
1.1%
Other values (374) 445379
88.1%
ValueCountFrequency (%)
31.66666667 1
< 0.1%
34.33333333 1
< 0.1%
55.33333333 1
< 0.1%
59.66666667 1
< 0.1%
61.66666667 2
< 0.1%
63.66666667 1
< 0.1%
64.33333333 1
< 0.1%
70 1
< 0.1%
73.33333333 1
< 0.1%
76 1
< 0.1%
ValueCountFrequency (%)
213.6666667 247
 
< 0.1%
213.3333333 1927
0.4%
213 2939
0.6%
212.6666667 3181
0.6%
212.3333333 3415
0.7%
212 3115
0.6%
211.6666667 3447
0.7%
211.3333333 3739
0.7%
211 3372
0.7%
210.6666667 4121
0.8%

Hydro_Road_Fire_Distance
Real number (ℝ)

High correlation 

Distinct12679
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4708.2733
Minimum108
Maximum13141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:35.240533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile1448
Q12800
median4270
Q36223
95-th percentile9595
Maximum13141
Range13033
Interquartile range (IQR)3423

Descriptive statistics

Standard deviation2478.0415
Coefficient of variation (CV)0.52631642
Kurtosis-0.029145393
Mean4708.2733
Median Absolute Deviation (MAD)1624
Skewness0.7353494
Sum2.3793447 × 109
Variance6140689.8
MonotonicityNot monotonic
2025-06-09T18:40:35.329816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2950 134
 
< 0.1%
3001 125
 
< 0.1%
2956 123
 
< 0.1%
2914 122
 
< 0.1%
5340 121
 
< 0.1%
3118 118
 
< 0.1%
3020 117
 
< 0.1%
3547 116
 
< 0.1%
3194 116
 
< 0.1%
2149 116
 
< 0.1%
Other values (12669) 504146
99.8%
ValueCountFrequency (%)
108 1
< 0.1%
115 1
< 0.1%
125 1
< 0.1%
150 2
< 0.1%
152 1
< 0.1%
157 1
< 0.1%
162 1
< 0.1%
164 1
< 0.1%
166 2
< 0.1%
180 1
< 0.1%
ValueCountFrequency (%)
13141 1
< 0.1%
13134 1
< 0.1%
13127 1
< 0.1%
13124 1
< 0.1%
13121 1
< 0.1%
13114 1
< 0.1%
13113 1
< 0.1%
13111 1
< 0.1%
13110 2
< 0.1%
13109 2
< 0.1%

Elevation_x_Slope
Real number (ℝ)

High correlation 

Distinct37825
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40100.483
Minimum0
Maximum204880
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:35.413080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11242.6
Q124696
median37308
Q352513
95-th percentile78532
Maximum204880
Range204880
Interquartile range (IQR)27817

Descriptive statistics

Standard deviation20984.36
Coefficient of variation (CV)0.52329443
Kurtosis0.91042054
Mean40100.483
Median Absolute Deviation (MAD)13658
Skewness0.80115176
Sum2.026494 × 1010
Variance4.4034335 × 108
MonotonicityNot monotonic
2025-06-09T18:40:35.493087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 621
 
0.1%
38376 163
 
< 0.1%
35508 159
 
< 0.1%
39312 157
 
< 0.1%
29520 154
 
< 0.1%
29780 146
 
< 0.1%
38844 146
 
< 0.1%
26748 143
 
< 0.1%
29590 143
 
< 0.1%
48960 142
 
< 0.1%
Other values (37815) 503380
99.6%
ValueCountFrequency (%)
0 621
0.1%
1929 1
 
< 0.1%
1940 1
 
< 0.1%
2012 1
 
< 0.1%
2088 2
 
< 0.1%
2100 1
 
< 0.1%
2103 1
 
< 0.1%
2121 1
 
< 0.1%
2122 1
 
< 0.1%
2129 1
 
< 0.1%
ValueCountFrequency (%)
204880 1
< 0.1%
203808 1
< 0.1%
201110 1
< 0.1%
195796 1
< 0.1%
193579 1
< 0.1%
189297 1
< 0.1%
185673 1
< 0.1%
184912 1
< 0.1%
179312 1
< 0.1%
177320 1
< 0.1%

Cover_Type
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0589349
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T18:40:35.547596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3893957
Coefficient of variation (CV)0.67481283
Kurtosis4.9724135
Mean2.0589349
Median Absolute Deviation (MAD)0
Skewness2.2809236
Sum1040491
Variance1.9304204
MonotonicityNot monotonic
2025-06-09T18:40:35.595376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 254165
50.3%
1 178709
35.4%
3 28058
 
5.6%
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
ValueCountFrequency (%)
1 178709
35.4%
2 254165
50.3%
3 28058
 
5.6%
4 2747
 
0.5%
5 9292
 
1.8%
6 14851
 
2.9%
7 17532
 
3.5%
ValueCountFrequency (%)
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
3 28058
 
5.6%
2 254165
50.3%
1 178709
35.4%

Interactions

2025-06-09T18:40:28.421779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:09.105238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:10.550612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:11.934443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:13.377182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:14.795414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.219772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:17.582815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:18.940453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:20.310466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:21.772839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:23.187541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:25.537045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:27.012267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:28.525069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:09.211517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:10.659700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:12.038658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:13.478695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:14.896923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.317122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:17.679926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:19.039728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:20.417806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:21.874098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:23.288551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:25.640550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:27.117578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:28.623586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:09.308032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:10.752539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:12.142166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:13.578628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:14.999044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.413635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:17.774439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:19.140008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:20.520334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:21.971395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:23.385930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:25.739529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:27.218756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:28.727448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:09.413524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:10.854059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:12.244430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:13.681134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:15.105849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.512157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:17.874720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:19.244240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:20.624629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:22.071904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:23.488438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:25.847038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:27.321274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:28.829564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:09.522049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:10.955179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:12.347942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:13.783190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:15.208361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.611159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:17.972718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:19.344554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:20.752750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:22.181720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:24.485609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:25.956943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:27.424789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:28.933692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:09.628186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:11.055691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:12.451938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:13.889203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:15.310198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.708666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:18.072233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:19.447061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:20.857822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:22.284236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:24.588130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:26.068800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:27.527717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:29.029770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:09.726492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:11.154142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:12.550443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:13.989508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:15.409719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.801185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:18.173144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:19.539593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:20.956773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:22.380188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:24.691422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:26.170310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:27.625243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:29.139199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:09.827000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:11.249362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:12.648546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:14.087749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:15.508221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.894440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:18.264652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:19.630894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:21.058069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:22.478408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:24.792099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:26.279825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:27.722765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:29.240138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:09.926202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:11.346879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:12.747822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:14.188260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:15.606758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.988214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:18.358040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:19.723014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:21.162483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:22.575924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:24.890608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:26.383361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:27.822970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:29.339658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:10.033439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:11.448438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:12.852340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:14.293590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:15.709275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:17.090607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:18.457551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:19.823523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:21.265992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:22.677804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:25.002390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:26.495253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:27.929216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:29.436853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:10.144962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:11.548628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:12.956663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:14.397102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:15.812583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:17.188617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:18.555062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:19.924800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:21.368347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:22.777315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:25.111717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:26.600792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:28.033551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:29.546151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:10.245845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:11.648151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:13.059899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:14.499620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:15.916099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:17.285131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:18.652002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:20.021065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:21.470863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:22.879630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:25.224001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:26.705653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:28.133822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:29.645657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:10.348370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:11.747089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:13.174420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:14.600465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.018415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:17.385972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:18.750512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:20.120433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:21.574371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:22.982975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:25.331524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:26.805820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:28.236153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:29.740508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:10.448676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:11.841203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:13.273672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:14.696984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:16.119260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:17.484499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:18.845436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:20.215941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:21.673321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:23.082292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:25.432522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:26.908345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T18:40:28.328658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-09T18:40:35.669885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Avg_HillshadeCover_TypeDistance_to_WaterElevationElevation_x_SlopeHillshade_3pmHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHydro_Road_Fire_DistanceSlopeSoil_Type_10Soil_Type_11Soil_Type_12Soil_Type_21Soil_Type_22Soil_Type_28Soil_Type_29Soil_Type_3Soil_Type_31Soil_Type_37Soil_Type_38Soil_Type_39Soil_Type_5Soil_Type_9Vertical_Distance_To_HydrologyWilderness_Area_0Wilderness_Area_2Wilderness_Area_3
Avg_Hillshade1.000-0.0780.0250.193-0.4990.655-0.1940.9860.0180.0360.2190.158-0.5230.0710.0870.0700.0450.1430.0740.0630.0450.1270.0490.0650.0450.0140.213-0.1090.0850.0380.230
Cover_Type-0.0781.0000.002-0.4930.084-0.028-0.009-0.063-0.121-0.007-0.226-0.2230.1670.1070.2050.1370.2010.1970.2060.1470.2580.1020.3630.3340.2220.3150.5140.1210.3450.1620.817
Distance_to_Water0.0250.0021.0000.2510.0790.036-0.0470.0220.0650.9990.0620.1430.0350.0410.0810.0160.0620.1910.0790.0540.0350.1400.0680.0440.1950.0410.0600.6470.1180.1690.098
Elevation0.193-0.4930.2511.000-0.0230.0730.0260.1860.1290.2610.4170.368-0.1750.1720.2860.1360.2160.1770.2340.1220.1890.1870.4090.3390.6280.3420.5470.0600.2880.2220.926
Elevation_x_Slope-0.4990.0840.079-0.0231.000-0.181-0.119-0.443-0.1320.057-0.155-0.1780.9840.0750.1950.2090.0410.1930.0630.0980.0770.1160.0390.1810.0950.0380.1720.3310.1890.1400.147
Hillshade_3pm0.655-0.0280.0360.073-0.1811.000-0.8190.566-0.0830.0350.1050.030-0.1870.0750.1440.2230.0440.1410.1000.1480.0330.1320.0640.0950.0450.0200.1530.0360.1990.1260.208
Hillshade_9am-0.194-0.009-0.0470.026-0.119-0.8191.000-0.0860.126-0.0390.0070.067-0.1250.0450.1240.1390.0490.1240.0910.1510.0440.1050.0470.0580.0190.0280.307-0.1320.2310.1340.278
Hillshade_Noon0.986-0.0630.0220.186-0.4430.566-0.0861.0000.0200.0320.2090.152-0.4680.0740.0850.0690.0390.1320.0840.0460.0700.1260.0470.0590.0460.0170.235-0.0990.1030.0640.236
Horizontal_Distance_To_Fire_Points0.018-0.1210.0650.129-0.132-0.0830.1260.0201.0000.0740.3710.735-0.1690.0760.2990.1160.0810.0900.2220.0740.0680.1330.0870.0560.0760.1160.217-0.0390.4070.2830.340
Horizontal_Distance_To_Hydrology0.036-0.0070.9990.2610.0570.035-0.0390.0320.0741.0000.0720.1540.0130.0420.0810.0200.0590.1870.0790.0550.0380.1430.0700.0420.1870.0430.0670.6240.1130.1650.107
Horizontal_Distance_To_Roadways0.219-0.2260.0620.417-0.1550.1050.0070.2090.3710.0721.0000.880-0.2280.1250.1040.1450.1500.0680.3210.1160.1030.1830.1090.1110.0910.1550.238-0.0320.4830.4220.398
Hydro_Road_Fire_Distance0.158-0.2230.1430.368-0.1780.0300.0670.1520.7350.1540.8801.000-0.2470.1020.2160.1520.1110.0970.3250.0950.1000.2190.1210.0960.0900.1950.3200.0020.5160.4200.543
Slope-0.5230.1670.035-0.1750.984-0.187-0.125-0.468-0.1690.013-0.228-0.2471.0000.0650.1760.2050.0640.2060.0900.1000.1070.1410.0690.0930.0420.0150.2650.3160.2160.1270.311
Soil_Type_100.0710.1070.0410.1720.0750.0750.0450.0740.0760.0420.1250.1020.0651.0000.0320.0210.0300.0420.0700.0320.0160.0410.0210.0190.0150.0150.0310.0220.1330.1410.000
Soil_Type_110.0870.2050.0810.2860.1950.1440.1240.0850.2990.0810.1040.2160.1760.0321.0000.0410.0580.0820.1360.0630.0310.0800.0420.0360.0290.0290.0600.0740.2430.2020.071
Soil_Type_120.0700.1370.0160.1360.2090.2230.1390.0690.1160.0200.1450.1520.2050.0210.0411.0000.0380.0540.0890.0410.0200.0520.0270.0240.0190.0190.0390.1060.1700.2040.046
Soil_Type_210.0450.2010.0620.2160.0410.0440.0490.0390.0810.0590.1500.1110.0640.0300.0580.0381.0000.0760.1260.0580.0290.0740.0380.0330.0270.0260.0550.0830.1150.1110.065
Soil_Type_220.1430.1970.1910.1770.1930.1410.1240.1320.0900.1870.0680.0970.2060.0420.0820.0540.0761.0000.1770.0820.0400.1040.0540.0470.0380.0370.0770.1530.0460.0230.092
Soil_Type_280.0740.2060.0790.2340.0630.1000.0910.0840.2220.0790.3210.3250.0900.0700.1360.0890.1260.1771.0000.1370.0670.1730.0900.0790.0640.0620.1290.0960.5260.4380.153
Soil_Type_290.0630.1470.0540.1220.0980.1480.1510.0460.0740.0550.1160.0950.1000.0320.0630.0410.0580.0820.1371.0000.0310.0800.0420.0360.0290.0290.0600.0270.2440.2030.071
Soil_Type_30.0450.2580.0350.1890.0770.0330.0440.0700.0680.0380.1030.1000.1070.0160.0310.0200.0290.0400.0670.0311.0000.0390.0200.0180.0140.0140.0290.0250.1280.1120.042
Soil_Type_310.1270.1020.1400.1870.1160.1320.1050.1260.1330.1430.1830.2190.1410.0410.0800.0520.0740.1040.1730.0800.0391.0000.0530.0460.0370.0360.0750.0490.3280.3730.089
Soil_Type_370.0490.3630.0680.4090.0390.0640.0470.0470.0870.0700.1090.1210.0690.0210.0420.0270.0380.0540.0900.0420.0200.0531.0000.0240.0190.0190.0390.0240.0130.0150.047
Soil_Type_380.0650.3340.0440.3390.1810.0950.0580.0590.0560.0420.1110.0960.0930.0190.0360.0240.0330.0470.0790.0360.0180.0460.0241.0000.0170.0170.0340.0550.0390.0140.041
Soil_Type_390.0450.2220.1950.6280.0950.0450.0190.0460.0760.1870.0910.0900.0420.0150.0290.0190.0270.0380.0640.0290.0140.0370.0190.0171.0000.0130.0280.2410.0280.0240.033
Soil_Type_50.0140.3150.0410.3420.0380.0200.0280.0170.1160.0430.1550.1950.0150.0150.0290.0190.0260.0370.0620.0290.0140.0360.0190.0170.0131.0000.0270.0550.1190.0930.409
Soil_Type_90.2130.5140.0600.5470.1720.1530.3070.2350.2170.0670.2380.3200.2650.0310.0600.0390.0550.0770.1290.0600.0290.0750.0390.0340.0280.0271.0000.0900.2450.0290.539
Vertical_Distance_To_Hydrology-0.1090.1210.6470.0600.3310.036-0.132-0.099-0.0390.624-0.0320.0020.3160.0220.0740.1060.0830.1530.0960.0270.0250.0490.0240.0550.2410.0550.0901.0000.1940.1660.106
Wilderness_Area_00.0850.3450.1180.2880.1890.1990.2310.1030.4070.1130.4830.5160.2160.1330.2430.1700.1150.0460.5260.2440.1280.3280.0130.0390.0280.1190.2450.1941.0000.8330.290
Wilderness_Area_20.0380.1620.1690.2220.1400.1260.1340.0640.2830.1650.4220.4200.1270.1410.2020.2040.1110.0230.4380.2030.1120.3730.0150.0140.0240.0930.0290.1660.8331.0000.227
Wilderness_Area_30.2300.8170.0980.9260.1470.2080.2780.2360.3400.1070.3980.5430.3110.0000.0710.0460.0650.0920.1530.0710.0420.0890.0470.0410.0330.4090.5390.1060.2900.2271.000

Missing values

2025-06-09T18:40:29.886707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-09T18:40:30.479539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area_0Wilderness_Area_2Wilderness_Area_3Soil_Type_3Soil_Type_5Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_21Soil_Type_22Soil_Type_28Soil_Type_29Soil_Type_31Soil_Type_37Soil_Type_38Soil_Type_39Distance_to_WaterAvg_HillshadeHydro_Road_Fire_DistanceElevation_x_SlopeCover_Type
02596.03.0258.00.0510.0221.0232.0148.06279.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.0258.000200.3337047.07788.05
12590.02.0212.0-6.0390.0220.0235.0151.06225.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.0212.085202.0006827.05180.05
22804.09.0268.065.03180.0234.0238.0135.06121.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.0275.770202.3339569.025236.02
32785.018.0242.0118.03090.0238.0238.0122.06211.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.0269.236199.3339543.050130.02
42595.02.0153.0-1.0391.0220.0234.0150.06172.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.0153.003201.3336716.05190.05
52579.06.0300.0-15.067.0230.0237.0140.06031.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.0300.375202.3336398.015474.02
62606.07.0270.05.0633.0222.0225.0138.06256.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.0270.046195.0007159.018242.05
72605.04.0234.07.0573.0222.0230.0144.06228.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.0234.105198.6677035.010420.05
82617.09.0240.056.0666.0223.0221.0133.06244.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.0246.447192.3337150.023553.05
92612.010.0247.011.0636.0228.0219.0124.06230.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.0247.245190.3337113.026120.05
ElevationSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area_0Wilderness_Area_2Wilderness_Area_3Soil_Type_3Soil_Type_5Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_21Soil_Type_22Soil_Type_28Soil_Type_29Soil_Type_31Soil_Type_37Soil_Type_38Soil_Type_39Distance_to_WaterAvg_HillshadeHydro_Road_Fire_DistanceElevation_x_SlopeCover_Type
5053443190.012.0190.014.01597.0228.0214.0117.01584.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0190.515186.3333371.038280.01
5053453183.016.0162.07.01595.0231.0205.0102.01608.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0162.151179.3333365.050928.01
5053463175.017.0134.020.01593.0227.0203.0104.01632.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0135.484178.0003359.053975.01
5053473169.015.0108.014.01591.0222.0205.0114.01657.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0108.904180.3333356.047535.01
5053483164.014.085.09.01590.0224.0209.0116.01681.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.085.475183.0003356.044296.01
5053493158.013.060.013.01590.0230.0211.0111.01706.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.061.392184.0003356.041054.01
5053503151.013.030.06.01590.0233.0214.0111.01731.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.030.594186.0003351.040963.01
5053513145.013.00.00.01591.0228.0213.0116.01756.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000185.6673347.040885.01
5053523140.016.00.00.01593.0221.0204.0114.01781.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000179.6673374.050240.01
5053533134.017.030.01.01595.0211.0200.0120.01806.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.030.017177.0003431.053278.01